Deep learning for the rapid automatic quantification and characterization of rotator cuff muscle degeneration from shoulder CT datasets.

This study aimed at developing a convolutional neural network (CNN) able to automatically quantify and characterize the level of degeneration of rotator cuff (RC) muscles from shoulder CT images including muscle atrophy and fatty infiltration. One hundred three shoulder CT scans from 95 patients with primary glenohumeral osteoarthritis undergoing anatomical total shoulder arthroplasty were retrospectively retrieved. Three independent radiologists manually segmented the premorbid boundaries of all four RC muscles on standardized sagittal-oblique CT sections. This premorbid muscle segmentation was further automatically predicted using a CNN. Automatically predicted premorbid segmentations were then used to quantify the ratio of muscle atrophy, fatty infiltration, secondary bone formation, and overall muscle degeneration. These muscle parameters were compared with measures obtained manually by human raters. Average Dice similarity coefficients for muscle segmentations obtained automatically with the CNN (88% ± 9%) and manually by human raters (89% ± 6%) were comparable. No significant differences were observed for the subscapularis, supraspinatus, and teres minor muscles (p > 0.120), whereas Dice coefficients of the automatic segmentation were significantly higher for the infraspinatus (p < 0.012). The automatic approach was able to provide good-very good estimates of muscle atrophy (R <sup>2</sup> = 0.87), fatty infiltration (R <sup>2</sup> = 0.91), and overall muscle degeneration (R <sup>2</sup> = 0.91). However, CNN-derived segmentations showed a higher variability in quantifying secondary bone formation (R <sup>2</sup> = 0.61) than human raters (R <sup>2</sup> = 0.87). Deep learning provides a rapid and reliable automatic quantification of RC muscle atrophy, fatty infiltration, and overall muscle degeneration directly from preoperative shoulder CT scans of osteoarthritic patients, with an accuracy comparable with that of human raters. • Deep learning can not only segment RC muscles currently available in CT images but also learn their pre-existing locations and shapes from invariant anatomical structures visible on CT sections. • Our automatic method is able to provide a rapid and reliable quantification of RC muscle atrophy and fatty infiltration from conventional shoulder CT scans. • The accuracy of our automatic quantitative technique is comparable with that of human raters

Physical properties of galaxies and their evolution in the VIMOS VLT Deep Survey. I. The evolution of the mass-metallicity relation up to z~0.9

We derive the mass-metallicity relation of star-forming galaxies up to $z\sim0.9$, using data from the VIMOS VLT Deep Survey. Automatic measurement of emission-line fluxes and equivalent widths have been performed on the full spectroscopic sample. This sample is divided into two sub-samples depending on the apparent magnitude selection: wide ($I_{\mathrm{AB}}<22.5$) and deep $I_{\mathrm{AB}}<24$). These two samples span two different ranges of stellar masses. Emission-line galaxies have been separated into star-forming galaxies and active galactic nuclei using emission line ratios. For the star-forming galaxies the emission line ratios have also been used to estimate gas-phase oxygen abundance, using empirical calibrations renormalized in order to give consistent results at low and high redshifts. The stellar masses have been estimated by fitting the whole spectral energy distributions with a set of stellar population synthesis models. We assume at first order that the shape of the mass-metallicity relation remains constant with redshift. Then we find a stronger metallicity evolution in the wide sample as compared to the deep sample. We thus conclude that the mass-metallicity relation is flatter at higher redshift. The observed flattening of the mass-metallicity relation at high redshift is analyzed as an evidence in favor of the open-closed model.Comment: 21 pages, revised version submitted to A&

The stellar masses and specific star-formation rates of submillimetre galaxies

Establishing the stellar masses (M*), and hence specific star-formation rates (sSFRs) of submillimetre galaxies (SMGs) is crucial for determining their role in the cosmic galaxy/star formation. However, there is as yet no consensus over the typical M* of SMGs. Specifically, even for the same set of SMGs, the reported average M* have ranged over an order of magnitude, from ~5x10^10 Mo to ~5x10^11 Mo. Here we study how different methods of analysis can lead to such widely varying results. We find that, contrary to recent claims in the literature, potential contamination of IRAC 3-8 um photometry from hot dust associated with an active nucleus is not the origin of the published discrepancies in derived M*. Instead, we expose in detail how inferred M* depends on assumptions made in the photometric fitting, and quantify the individual and cumulative effects of different choices of initial mass function, different brands of evolutionary synthesis models, and different forms of assumed star-formation history. We review current observational evidence for and against these alternatives as well as clues from the hydrodynamical simulations, and conclude that, for the most justifiable choices of these model inputs, the average M* of SMGs is ~2x10^11 Mo. We also confirm that this number is perfectly reasonable in the light of the latest measurements of their dynamical masses, and the evolving M* function of the overall galaxy population. M* of this order imply that the average sSFR of SMGs is comparable to that of other star-forming galaxies at z>2, at 2-3 Gyr^-1. This supports the view that, while rare outliers may be found at any M*, most SMGs simply form the top end of the main-sequence of star-forming galaxies at these redshifts. Conversely, this argues strongly against the viewpoint that SMGs are extreme pathological objects, of little relevance in the cosmic history of star-formation.Comment: Accepted to A&A. 13 pages, 5 figures, 3 tables. Main changes: 1) investigation that the main-sequence does not change the location as much as SMGs when changing SFHs; 2) a new table added with all stellar mass estimates for individual SMGs (machine-readable version in the source file). V3: missing references adde

Evolution of the Most Massive Galaxies to z=0.6: I. A New Method for Physical Parameter Estimation

We use principal component analysis (PCA) to estimate stellar masses, mean stellar ages, star formation histories (SFHs), dust extinctions and stellar velocity dispersions for ~290,000 galaxies with stellar masses greater than $10^{11}Msun and redshifts in the range 0.4<z<0.7 from the Baryon Oscillation Spectroscopic Survey (BOSS). We find the fraction of galaxies with active star formation first declines with increasing stellar mass, but then flattens above a stellar mass of 10^{11.5}Msun at z~0.6. This is in striking contrast to z~0.1, where the fraction of galaxies with active star formation declines monotonically with stellar mass. At stellar masses of 10^{12}Msun, therefore, the evolution in the fraction of star-forming galaxies from z~0.6 to the present-day reaches a factor of ~10. When we stack the spectra of the most massive, star-forming galaxies at z~0.6, we find that half of their [OIII] emission is produced by AGNs. The black holes in these galaxies are accreting on average at ~0.01 the Eddington rate. To obtain these results, we use the stellar population synthesis models of Bruzual & Charlot (2003) to generate a library of model spectra with a broad range of SFHs, metallicities, dust extinctions and stellar velocity dispersions. The PCA is run on this library to identify its principal components over the rest-frame wavelength range 3700-5500A. We demonstrate that linear combinations of these components can recover information equivalent to traditional spectral indices such as the 4000A break strength and HdA, with greatly improved S/N. This method is able to recover physical parameters such as stellar mass-to-light ratio, mean stellar age, velocity dispersion and dust extinction from the relatively low S/N BOSS spectra. We examine the sensitivity of our stellar mass estimates to the input parameters in our model library and the different stellar population synthesis models.Comment: 20 pages, 18 Figures, submitted to MNRA

Physical interpretation of the near-infrared colours of low-redshift galaxies

International audienceWe use empirical techniques to interpret the near-infrared (near-IR) colours of a sample of 5800 galaxies drawn from Sloan Digital Sky Survey (SDSS) main spectroscopic sample with YJHK photometry from the United Kingdom Infrared Deep Sky Survey (UKIDSS) data release 1. Our study focuses on the inner 3 arcsec regions of the galaxies sampled by the SDSS fibre spectra. We study correlations between near-IR colours measured within this aperture and physical parameters derived from the spectra. These parameters include specific star formation rate (SFR), stellar age, metallicity and dust attenuation. All correlations are analysed for samples of galaxies that are closely matched in redshift, in stellar mass and in concentration index. Whereas more strongly star-forming galaxies have bluer optical colours, the opposite is true at near-IR wavelengths - galaxies with higher specific SFR have redder near-IR colours. This result agrees qualitatively with the predictions of models in which thermally pulsing asymptotic giant branch (TP-AGB) stars dominate the H- and K-band light of a galaxy following a burst of star formation. We also find a surprisingly strong correlation between the near-IR colours of star-forming galaxies and their dust attenuation as measured from the Balmer decrement. Unlike optical colours, however, near-IR colours exhibit very little dependence on galaxy inclination. This suggests that the correlation of near-IR colours with dust attenuation arises because TP-AGB stars are the main source of dust in the galaxy. Finally, we compare the near-IR colours of the galaxies in our sample to the predictions of three different stellar population models: the Bruzual & Charlot model, a preliminary version of a new model under development which includes a new prescription for AGB star evolution, and the Maraston model

Galaxy and mass assembly (GAMA): colour- and luminosity-dependent clustering from calibrated photometric redshifts

We measure the two-point angular correlation function of a sample of 4289 223 galaxies with r < 19.4 mag from the Sloan Digital Sky Survey (SDSS) as a function of photometric redshift, absolute magnitude and colour down to Mr − 5 log h = −14 mag. Photometric redshifts are estimated from ugriz model magnitudes and two Petrosian radii using the artificial neural network package ANNz, taking advantage of the Galaxy And Mass Assembly (GAMA) spectroscopic sample as our training set. These photometric redshifts are then used to determine absolute magnitudes and colours. For all our samples, we estimate the underlying redshift and absolute magnitude distributions using Monte Carlo resampling. These redshift distributions are used in Limber's equation to obtain spatial correlation function parameters from power-law fits to the angular correlation function. We confirm an increase in clustering strength for sub-L* red galaxies compared with ∼L* red galaxies at small scales in all redshift bins, whereas for the blue population the correlation length is almost independent of luminosity for ∼L* galaxies and fainter. A linear relation between relative bias and log luminosity is found to hold down to luminosities L ∼ 0.03L*. We find that the redshift dependence of the bias of the L* population can be described by the passive evolution model of Tegmark & Peebles. A visual inspection of a random sample from our r < 19.4 sample of SDSS galaxies reveals that about 10 per cent are spurious, with a higher contamination rate towards very faint absolute magnitudes due to over-deblended nearby galaxies. We correct for this contamination in our clustering analysis